1 Overview

For these exploratory runs there were 100 objectives to optimize, all with the target of 100 and an accuracy requirement of 99%. The population was size 512, and there was a point mutation probability of .7% at each objective.

2 Mu Lambda Selection

2.1 Time Series Data

2.1.1 Population Trait Average Performance

2.1.2 Population Average Maximum Trait Performance

2.1.3 Population Average Optimal Trait Count

2.1.4 Population Average Maximum Optimal Trait Count

2.2 End Of Run Data

2.2.1 Population Trait Average Performance

2.2.2 Population Average Maximum Trait Performance

2.2.3 Population Average Optimal Trait Count

2.2.4 Population Average Maximum Optimal Trait Count

2.3 Best Value Throughout Run

2.3.1 Population Trait Average Performance

2.3.2 Population Best Trait Average Performance

2.3.3 Population Average Optimal Trait Count

2.3.4 Population Best Average Optimal Trait Count

2.3.5 Population Unique Optimal Traits

3 Tournament

3.1 Time Series Data

3.1.1 Population Trait Average Performance

3.1.2 Population Maximum Average Trait Performance

3.1.3 Population Average Optimal Trait Count

3.1.4 Population Average Maimum Optimal Trait Count

3.2 End Of Run Data

3.2.1 Population Trait Average Performance

3.2.2 Population Average Maximum Trait Performance

3.2.3 Population Average Optimal Trait Count

3.2.4 Population Average Maximum Optimal Trait Count

3.3 Best Value Throughout Run

3.3.1 Population Trait Average Performance

3.3.2 Population Best Trait Average Performance

3.3.3 Population Average Optimal Trait Count

3.3.4 Population Best Average Optimal Trait Count

3.3.5 Population Unique Optimal Traits

4 Lexicase

4.1 Time Series Data

4.1.1 Population Average Trait Performance

4.1.2 Population Average Maximum Trait Performance

4.1.3 Population Average Optimal Trait Count

4.1.4 Population Average Maimum Optimal Trait Count

4.2 End Of Run Data

4.2.1 Population Trait Average Performance

4.2.2 Population Average Maximum Trait Performance

4.2.3 Population Average Optimal Trait Count

4.2.4 Population Average Maximum Optimal Trait Count

4.3 Best Value Throughout Run

4.3.1 Population Trait Average Performance

4.3.2 Population Best Trait Average Performance

4.3.3 Population Average Optimal Trait Count

4.3.4 Population Best Average Optimal Trait Count

4.3.5 Population Unique Optimal Traits

5 Fitness Sharing

5.1 Time Series Data

5.1.1 Population Average Trait Performance

5.1.2 Population Average Maximum Trait Performance

5.1.3 Population Average Optimal Trait Count

5.1.4 Population Average Maimum Optimal Trait Count

5.2 End Of Run Data

5.2.1 Population Trait Average Performance

5.2.2 Population Average Maximum Trait Performance

5.2.3 Population Average Optimal Trait Count

5.2.4 Population Average Maximum Optimal Trait Count

5.3 Best Value Throughout Run

5.3.1 Population Trait Average Performance

5.3.2 Population Best Trait Average Performance

5.3.3 Population Average Optimal Trait Count

5.3.4 Population Best Average Optimal Trait Count

5.3.5 Population Unique Optimal Traits

6 Novelty Euclidean

6.1 Time Series Data

6.1.1 Population Average Trait Performance

6.1.2 Population Average Maximum Trait Performance

6.1.3 Population Average Optimal Trait Count

6.1.4 Population Average Maimum Optimal Trait Count

6.2 End Of Run Data

6.2.1 Population Trait Average Performance

6.2.2 Population Average Maximum Trait Performance

6.2.3 Population Average Optimal Trait Count

6.2.4 Population Average Maximum Optimal Trait Count

6.3 Best Value Throughout Run

6.3.1 Population Trait Average Performance

6.3.2 Population Best Trait Average Performance

6.3.3 Population Average Optimal Trait Count

6.3.4 Population Best Average Optimal Trait Count

6.3.5 Population Unique Optimal Traits